Polynomial Signal Processing

By V. J. Mathews, G. Sicuranza. 2000

632 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f0c9074ee5b0e619ba884cf0d091e890b14abb2d

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Citations in the corpus

Behavioral modeling of RF power amplifiers based on pruned volterra series (2004, background): “However, high computational complexity makes standard methods of this kind limited to “weak” nonlinearity [1], [2], e.”
Dynamic Deviation Reduction-Based Volterra Behavioral Modeling of RF Power Amplifiers (2006, background): “It is also a built-in feature of most of the latest signal generators.”
Online Prediction of Time Series Data With Kernels (2009, background): “Two main types of nonlinear models have been extensively studied over the years: polynomial ﬁlters, usually called Volterra series based ﬁlters [6], and neural networks [7].”
Transmitted-reference UWB systems using weighted autocorrelation receivers (2006, background): “The relation between the AcR output and transmitted symbols is namely a finite-impulse-response (FIR) Volterra channel [15], [16] instead of a linear FIR channel.”
A comparative overview of microwave and wireless power-amplifier behavioral modeling approaches (2005, background): “Envisaging future nonlinearity compensation schemes [6], [43], the authors suggested the use of an adaptive learning process, in detriment to a more simple and obvious direct extraction [33], [44].”
Digital Predistortion for Envelope-Tracking Power Amplifiers Using Decomposed Piecewise Volterra Series (2008, background): “This nonlinear behavior is very different from what we normally see in the typical linear amplifiers with a fixed supply voltage.”